A system and method determines the creditworthiness of a consumer and issues a loan and generates a behavioral profile for that consumer. An initial set of data is acquired from the consumer that includes non-identification attributes without obtaining a full name, a credit card number, a passport number, or a government issued ID number that allows identification of the consumer. A user ID number matches the initial set of data to a physical user in a transaction database. A credit score based on the average credit among a plurality of user profiles is matched to determine a maximum credit for the consumer. A machine learning model may be applied to stored consumer loan data to determine when the consumer requires an increase in the maximum allowed credit and the risk involved with increasing the maximum allowed credit.
Legal claims defining the scope of protection, as filed with the USPTO.
1. A method of determining the creditworthiness and issuing a micro- or nano loan to a consumer and forecasting a bad debt probability of that consumer, comprising: a consumer selecting and connecting a mobile wireless communications device of the consumer via a wireless communications network to a server having a communications module, a controller, transactional database connected thereto, and an application programming interface (API) operative to allow interaction between the server and the mobile wireless communications device, in response to the consumer's selecting and connecting to the server, the controller initiates via the API a user interface on a display of the mobile wireless communications device, the user interface displaying a first menu item as a button selection on a portion of the display for requesting a micro- or nano loan, wherein the consumer selects the first menu item and initiates an API call as a request for a micro- or nano loan; in response to the consumer selecting the first menu item and initiating the loan request, the server extracts N attributes about the consumer from external public data sources, wherein the N attributes have no personal identification data and confidential information about the consumer and comprise anonymous consumer transaction data extracted from transactional platforms and data extracted from one or more of a) gender, b) age, c) cellular operator, phone model, and usage, d) consumer geolocation, e) home values by geolocation, f) average income by: geolocation, gender and age groups, g) education by: geolocation and gender, h) public transport options by geolocation, i) social media activities by: geolocation, gender and age groups, j) infrastructure and services available by geolocation, and k) criminal records by geolocation, wherein the attributes are extracted from the external public data sources without obtaining a full name, a credit card number, a passport number, or a government issued ID number and other data that allows identification of the consumer; processing the N attributes at the server by applying a features construction model and transforming the N attributes into a user attribute string associated with the consumer; matching the user attribute string associated with the consumer with user attribute strings stored within the transactional database and associated with other consumers, wherein a match to another user attribute string stored within the transactional database is indicative of the micro- or nano loan amount as a maximum credit limit that is loaned to the consumer initially requesting the loan; transmitting to the mobile wireless communications device via the communications module of the server a loan approval code, and in response to receiving the loan approval code at the mobile wireless communications device, displaying on the user interface a second menu item as button selections for confirming and selecting a micro- or nano loan amount up to the maximum credit allowed for the consumer and how the loan is to be dispersed as either crediting an electronic wallet of the consumer or paying all or part of a bill associated with an account of the consumer in the value of the loan; in response to the consumer selecting the second menu item and confirming and selecting a micro- or nano loan amount up to the maximum credit allowed for the consumer and how the loan is to be dispersed, the server credits the electronic wallet of the consumer or pays all or part of a bill associated with an account of the consumer in the value of the loan based upon the consumer's selection at the second menu item, wherein the micro- or nano loan is approved on an average in under 20 seconds and with no more than three selectings entered by the consumer on the mobile wireless communications device; generating a user ID associated with the user attribute string of the consumer and storing the user ID and user attribute string within the transactional database; acquiring additional attributes linked to transactions made by the consumer over a plurality of weeks; linking the additional attributes to the consumer's user attribute string stored in the transactional database; and applying a bad debt prediction model to the additional attributes and user attribute string to generate a bad debt prediction for the consumer as a numerical indicia, and if the numerical indicia is below a threshold value, raising the credit limit for the consumer.
2. The method according to claim 1 , wherein the additional attributes include data associated with previous purchasing transactions of the consumer over the plurality of weeks, wherein the bad debt prediction model comprises a regression model having a moving window that takes into account mean, standard deviation, median, kurtosis and skewness.
3. The method according to claim 2 , wherein the controller is configured to input past input/output data to the bad debt prediction model, wherein the past input/output data comprises a vector for the input relating to past consumer loan data and an output relating to a probability between 0 and 1 that indicates whether a consumer will fall into bad debt.
4. The method according to claim 3 , wherein a probability greater than 0.6 from the bad debt prediction model is indicative of a high risk that a consumer will fall into bad debt.
5. The method according to claim 3 , wherein a target variable outcome from the bad debt prediction model comprises a binary outcome that indicates whether a consumer will be a risk of bad debt within seven days.
6. The method according to claim 3 , wherein the controller is configured to collect consumer loan data over a period of six months and classify consumers in two classes as 1) a bad client having a high risk probability of falling into bad debt, and 2) a good client having a low risk probability of falling into bad debt.
7. The method according to claim 1 , wherein the controller is configured to generate a behavioral prediction of the consumer and match consumer location and check-ins to at least one of the electronic wallet and the location of the consumer against a known-locations database incorporated within the transactional database and comprising data regarding stores, private locations, public places, and transaction data and correlate periodic location patterns to loan and transactional activities by consumer profile and periodicity; loan disbursement patterns; use of loans; loan repayments; and transaction activities.
8. The method according to claim 7 , wherein the controller is configured to generate the behavioral prediction based on consumer segmentation with consumer information provided via the contents of each transaction, and using affinity and purchase path analysis to identify products that sell in conjunction with each other depending on promotional and seasonal basis and linking between purchases over time.
9. A system of determining the creditworthiness and issuing a micro- or nano loan to a consumer and forecasting a bad debt probability of that consumer, comprising: a mobile wireless communications device of the consumer; a server having a communications module, controller, a transactional database connected thereto, and an application programming interface (API); a wireless communications network connected to said server and mobile wireless communications device, wherein said API of said server is operative to allow interaction between the server and the mobile wireless communications device, wherein said controller and communications module are operative to communicate with the consumer operating the mobile wireless communications device via the wireless communications network and in response to the consumer's selecting and connecting to the server, the server initiates via the API a user interface on a display of the mobile wireless communications device, the user interface displaying a first menu item as a button selection on a portion of the display for requesting a micro- or nano loan via the first menu item and initiating an API call as a request for a micro- or nano loan; in response to the consumer selecting the first menu item and initiating the loan request, the server is configured to extract N attributes about the consumer from external public data sources, wherein the N attributes have no personal identification data and confidential information about the consumer and comprises anonymous consumer transaction data extracted from transactional platforms and data extracted from one or more of a) gender, b) age, c) cellular operator, phone model, and usage, d) consumer geolocation, e) home values by geolocation, f) average income by: geolocation, gender and age groups, g) education by: geolocation and gender, h) public transport options by geolocation, i) social media activities by: geolocation, gender and age groups, j) infrastructure and services available by geolocation, and k) criminal records by geolocation, wherein the N attributes are extracted from the external public data sources without obtaining a full name, a credit card number, a passport number, or a government issued ID number and other data that allows identification of the consumer, wherein the server is configured to: process the N attributes at the server and apply a features construction model and transform the N attributes into a user attribute string associated with the consumer; match the user attribute string associated with the consumer with user attribute strings stored within the transactional database and associated with other consumers, wherein a match to another user attribute string stored within the transactional database is indicative of the micro- or nano loan amount as a maximum credit limit that is loaned to the consumer initially requesting the loan; transmit to the mobile wireless communications device via the communications module of the server a loan approval code, and in response to receiving the loan approval code at the mobile wireless communications device, the mobile wireless communications device displays on the user interface a second menu item as button selections for confirming and selecting a micro- or nano loan amount up to the maximum credit allowed for the consumer and how the loan is to be dispersed as either crediting an electronic wallet of the consumer or paying all or part of a bill associated with an account of the consumer in the value of the loan; in response to the consumer selecting the second menu item and confirming and selecting a micro- or nano loan amount up to the maximum credit allowed for the consumer and how the loan is to be dispersed, the server credits the electronic wallet of the consumer or pays all or part of a bill associated with an account of the consumer in the value of the loan based upon the consumer's selection at the second menu item, wherein the micro-or nano loan is approved on an average in under 20 seconds and with no more than three selectings entered by the consumer on the mobile wireless communications device; and wherein the controller is configured to: generate a user ID associated with the user attribute string of the consumer and store the user ID and user attribute string within the transactional database; acquire additional attributes linked to the transactions made by the consumer over a plurality of weeks; link the additional attributes to the consumer's user attribute string stored in the transactional database; and apply a bad debt prediction model to the additional attributes and user attribute string to generate a bad debt prediction for the consumer as a numerical indicia, and if the numerical indicia is below a threshold value, the credit limit is raised for the consumer.
10. The system according to claim 9 , wherein the additional attributes include data associated with previous purchasing transactions of the consumer over the plurality of weeks, and wherein the bad debt prediction model comprises a regression model having a moving window that takes into account mean, standard deviation, median, kurtosis and skewness.
11. The system according to claim 10 , wherein said controller is configured to input past input/output data about the additional attributes to the bad debt prediction model, wherein the past input/output data comprises a vector for the input relating to past consumer loan data and an output relating to a probability between 0 and 1 that indicates whether a consumer will fall into bad debt.
12. The system according to claim 11 , wherein a probability greater than 0.6 is indicative of a high risk that a consumer will fall into bad debt.
13. The system according to claim 12 , wherein a target variable outcome from the bad debt prediction model comprises a binary outcome that indicates whether a consumer will be a risk of bad debt within seven days.
14. The system according to claim 9 , wherein said controller is configured to collect the additional attributes over a period of six months and classify consumers in two classes as 1) a bad client having a high risk probability of falling into bad debt, and 2) a good client having a low risk probability of falling into bad debt.
15. The system according to claim 9 , wherein said controller is configured to generate a behavioral prediction of the consumer and match consumer location and check-ins to at least one of the electronic wallet and the location of the consumer against a known-locations database incorporated within the transactional database and comprising data regarding stores, private locations, public places, and transaction data and correlate periodic location patterns to loan and transactional activities and predict by consumer profile and periodicity; loan disbursement patterns; use of loans; loan repayments; and transaction activities.
16. The system according to claim 9 , wherein said controller is configured to generate the behavioral prediction based on consumer segmentation with consumer information provided via the contents of each transaction and use affinity and purchase path analysis to identify products that sell in conjunction with each other depending on promotional and seasonal basis and linking between purchases over time.
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April 27, 2018
December 29, 2020
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